code: def get_synth_data(height, width, num_channels, num_classes, dtype):
      """Creates a set of synthetic random data.
  
      Args:
        height: Integer height that will be used to create a fake image tensor.
        width: Integer width that will be used to create a fake image tensor.
        num_channels: Integer depth that will be used to create a fake image tensor.
        num_classes: Number of classes that should be represented in the fake labels
          tensor
        dtype: Data type for features/images.
  
      Returns:
        A tuple of tensors representing the inputs and labels.
  
      """
      # Synthetic input should be within [0, 255].
      inputs = tf.random.truncated_normal(
          [height, width, num_channels],
          dtype=dtype,
          mean=127,
          stddev=60,
          name="synthetic_inputs",
      )
      labels = tf.random.uniform(
          [1],
          minval=0,
          maxval=num_classes - 1,
          dtype=tf.int32,
          name="synthetic_labels",
      )
      return inputs, labels
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import flags
  import numpy
  import tensorflow
  from tensorflow.python.keras.optimizer_v2 import gradient_descent
  from utils.flags import core
  from utils.misc import keras_utils
label_desc: Use image node_id 6 for calling a global function with example usage: get_synth_data(height, width, num_channels, num_classes, dtype) and returns: inputs,  labels